Nonparametric methods for modeling GCM and scenario uncertainty in drought assessment

被引:106
作者
Ghosh, Subimal [1 ]
Mujumdar, P. P. [1 ]
机构
[1] Indian Inst Sci, Dept Civil Engn, Bangalore 560012, Karnataka, India
关键词
D O I
10.1029/2006WR005351
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
[1] Hydrologic implications of global climate change are usually assessed by downscaling appropriate predictors simulated by general circulation models (GCMs). Results from GCM simulations are subjected to a number of uncertainties due to incomplete knowledge about the underlying geophysical processes of global change (GCM uncertainties) and due to uncertain future scenarios (scenario uncertainties). With a relatively small number of GCMs available and a finite number of scenarios simulated by them, uncertainties in the hydrologic impacts at a smaller spatial scale become particularly pronounced. In this paper, a methodology is developed to address such uncertainties for a specific problem of drought impact assessment with results from GCM simulations. Samples of a drought indicator are generated with downscaled precipitation from available GCMs and scenarios. Since it is very unlikely that such small samples resulting from GCM scenarios fit a known parametric distribution, nonparametric methods such as kernel density estimation and orthonormal series methods are used to determine the probability distribution function (PDF) of the drought indicator. Principal component analysis, fuzzy clustering, and statistical regression are used for downscaling the mean sea level pressure (MSLP) output from the GCMs to precipitation at a smaller spatial scale. Reanalysis data from the National Center for Environmental Prediction (NCEP) are used in relating precipitation with MSLP. The information generated through the PDF of the drought indicator in a future year may be used in long-term planning decisions. The methodology is demonstrated with a case study of the drought-prone Orissa meteorological subdivision in India.
引用
收藏
页数:19
相关论文
共 73 条
[1]   Trends in 20th century drought over the continental United States [J].
Andreadis, Konstantinos M. ;
Lettenmaier, Dennis P. .
GEOPHYSICAL RESEARCH LETTERS, 2006, 33 (10)
[2]  
[Anonymous], 1993, J IRRIG DRAIN ENG, V119, P429
[3]   Fuzzy rule-based downscaling of precipitation [J].
Bardossy, A ;
Bogardi, I ;
Matyasovszky, I .
THEORETICAL AND APPLIED CLIMATOLOGY, 2005, 82 (1-2) :119-129
[4]   SPACE-TIME MODEL FOR DAILY RAINFALL USING ATMOSPHERIC CIRCULATION PATTERNS [J].
BARDOSSY, A ;
PLATE, EJ .
WATER RESOURCES RESEARCH, 1992, 28 (05) :1247-1259
[5]   FUZZY RULE-BASED CLASSIFICATION OF ATMOSPHERIC CIRCULATION PATTERNS [J].
BARDOSSY, A ;
DUCKSTEIN, L ;
BOGARDI, I .
INTERNATIONAL JOURNAL OF CLIMATOLOGY, 1995, 15 (10) :1087-1097
[6]   MODELING DAILY RAINFALL USING A SEMI-MARKOV REPRESENTATION OF CIRCULATION PATTERN OCCURRENCE [J].
BARDOSSY, A ;
PLATE, EJ .
JOURNAL OF HYDROLOGY, 1991, 122 (1-4) :33-47
[7]   Downscaling from GCMs to local climate through stochastic linkages [J].
Bardossy, A .
JOURNAL OF ENVIRONMENTAL MANAGEMENT, 1997, 49 (01) :7-17
[8]   Tentative probabilistic temperature scenarios for northern Europe [J].
Benestad, RE .
TELLUS SERIES A-DYNAMIC METEOROLOGY AND OCEANOGRAPHY, 2004, 56 (02) :89-101
[9]  
Bezdek J. C., 1981, Pattern recognition with fuzzy objective function algorithms
[10]   Downscaling recent streamflow conditions in British Columbia, Canada using ensemble neural network models [J].
Cannon, AJ ;
Whitfield, PH .
JOURNAL OF HYDROLOGY, 2002, 259 (1-4) :136-151